No, all the weights, all the “data” essentially has to be in RAM. If you “talk to” a LLM on your GPU, it is not making any calls to the internet, but making a pass through all the weights every time a word is generated.
There are system to augment the prompt with external data (RAG is one word for this), but fundamentally the system is closed.
Yeah, I’ve had decent results running the 7B/8B models, particularly the fine tuned ones for specific use cases. But as ya mentioned, they’re only really good in thier scope for a single prompt or maybe a few follow-ups. I’ve seen little improvement with the 13B/14B models and find them mostly not worth the performance hit.
Depends which 14B. Arcee’s 14B SuperNova Medius model (which is a Qwen 2.5 with some training distilled from larger models) is really incrtedible, but old Llama 2-based 13B models are awful.
Try a new quantization as well! Like an IQ4-M depending on the size of your GPU, or even better, an 4.5bpw exl2 with Q6 cache if you can manage to set up TabbyAPI.
Unfortunately that’s not really relevant to LLMs beyond inserting things into the text you feed them. For every single word they predict, they make a pass through the multi-gigabyte weights. Its largely memory bound, and not integrated with any kind of sane external memory algorithm.
There are some techniques that muddy this a bit, like MoE and dynamic lora loading, but the principle is the same.
Aren’t LLMs external algorithms at this point? As in the all data will not fit in RAM.
No, all the weights, all the “data” essentially has to be in RAM. If you “talk to” a LLM on your GPU, it is not making any calls to the internet, but making a pass through all the weights every time a word is generated.
There are system to augment the prompt with external data (RAG is one word for this), but fundamentally the system is closed.
Yeah, I’ve had decent results running the 7B/8B models, particularly the fine tuned ones for specific use cases. But as ya mentioned, they’re only really good in thier scope for a single prompt or maybe a few follow-ups. I’ve seen little improvement with the 13B/14B models and find them mostly not worth the performance hit.
Depends which 14B. Arcee’s 14B SuperNova Medius model (which is a Qwen 2.5 with some training distilled from larger models) is really incrtedible, but old Llama 2-based 13B models are awful.
I’ll try it out! It’s been a hot minute, and it seems like there are new options all the time.
Try a new quantization as well! Like an IQ4-M depending on the size of your GPU, or even better, an 4.5bpw exl2 with Q6 cache if you can manage to set up TabbyAPI.
No, I’m talking about https://en.m.wikipedia.org/wiki/External_memory_algorithm
Unrelated to RAGs
Unfortunately that’s not really relevant to LLMs beyond inserting things into the text you feed them. For every single word they predict, they make a pass through the multi-gigabyte weights. Its largely memory bound, and not integrated with any kind of sane external memory algorithm.
There are some techniques that muddy this a bit, like MoE and dynamic lora loading, but the principle is the same.